agentleFS
fxsteinfxstein/ai-todo/.cursor/rules/linear-release-workflow.mdc

ai-todo / rules

Cursor rule16 starsChanged 10 months agoApache-2.0View on GitHub

Automated release workflow with Linear tracking and approval gates

  • Commits and pushes

The file

We split this linear-release-workflow.mdc into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Update Linear issue to "In Progress"

    line 15
  2. Distinctive:

    Mark in progress: Start root task and first subtask before beginning

    line 17
  3. Distinctive:

    If feedback about the document or version number is provided, make the changes as necessary and edit the release summary then go back to …

    line 30
  4. Distinctive:

    Update Linear issue to "Done"

    line 40

≈ “Common” is an estimate: at least 1 in 5 of the 6,668 Cursor rule files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
linear-release-workflow.mdc
---
description: "Automated release workflow with Linear tracking and approval gates"
alwaysApply: false # DISABLED
---

# Automated Release Workflow

**Trigger:** "start release" or "release <version>" with Linear issue ID

## Workflow Steps

1. **Create ai-todo task structure:**
   - Root task: `[<AIT-ID>] Release <version>`
   - Subtasks: Generate summary → Prepare → Approval gate → Execute → Monitor → Finalize
   - Update Linear issue to "In Progress"

2. **Mark in progress:** Start root task and first subtask before beginning

3. **Generate summary:** `release/AI_RELEASE_SUMMARY.md` (2-3 paragraphs, user-facing changes)

4. **Prepare:** Run `./release/release.sh --prepare [--beta] --summary release/AI_RELEASE_SUMMARY.md`

5. **Commit & push:** `git add release/ && git commit && git push`

6. **Update Linear:** Comment with summary link, request approval

7. **Monitor Linear for approval:**
   - Poll `user-linear.list_comments` every 60s
   - Keywords: "Approve", "Proceed", "Execute" = continue | "Abort" = stop
   - If feedback about the document or version number is provided, make the changes as necessary and edit the release summary then go back to Commit & push and update linear with the latest changes and monitor linear again for approval
   - Complete approval subtask when approved

8. **Execute:** Run `./release/release.sh --execute`

9. **Monitor CI:** Poll `gh run list` until completion (30s intervals)

10. **Finalize:**
    - Complete all tasks
    - Comment on Linear with release summary (PyPI link, GitHub release, duration)
    - Update Linear issue to "Done"

## Safety Rules

- ✅ Mark subtask as in-progress when starting each step
- ✅ **WAIT and MONITOR** at approval gate until explicit Linear comment
- ✅ **STOP** on ANY error - report to Linear, wait for user
- ❌ Never skip approval step
- ❌ Never proceed if CI fails
- ❌ Never use `--no-verify`

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.